import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from matplotlib import collections
import seaborn as sns
import os
from pathlib import Path
import glob
import re
from cycler import cycler
import itertools
import fiona
import geopandas as gp
from shapely.geometry import LineString, Point, Polygon
%matplotlib inline
sns.color_palette()
sns.set_style("white", {"xtick.direction": "in","ytick.direction": "in"})
plt.rcParams['xtick.bottom'] = True
plt.rcParams['ytick.left'] = True
plt.rcParams['axes.prop_cycle'] = plt.cycler(color=plt.cm.Set2.colors[:6])
my_tiel = (0.4, 0.7607843137254902, 0.6470588235294118) # Recycling
my_orange = (0.9882352941176471, 0.5529411764705883, 0.3843137254901961) # Manufacturing
my_purple = (0.5529411764705883, 0.6274509803921569, 0.796078431372549) # 40209
my_pink = (0.9058823529411765, 0.5411764705882353, 0.7647058823529411) #NAICS
The files I am going to load are the ones showed in the table below. This is to know which file correspond to what. I have not added the dates in the file name since they are autogenerated and it is a sanity check for me, but in reality the dates (numbers in front of the file name scenario) don't matter.
cwd = os.getcwd()
facility_label = ['Manufacturing', 'Recycling', 'Manufacturing_cap', 'Recycling_cap']
location_label = ['NAICS', '40209']
factor_label_rec = ['05', '1', '2', '5', '10']
factor_label_man = ['0001','05', '1', '2'] # 0 is 0,5
files_list = []
for fac in facility_label:
for loc in location_label:
if fac.startswith('Manufacturing'):
for fac_man in factor_label_man:
files_list.append(fac+'_'+loc+'_'+fac_man)
else:
for fac_rec in factor_label_rec:
files_list.append(fac+'_'+loc+'_'+fac_rec)
files_list.remove('Manufacturing_cap_NAICS_0001')
files_list.remove('Manufacturing_cap_40209_0001')
recycling_files = [x for x in files_list if x.startswith('Recycling')]
recycling_files_cap = [x for x in recycling_files if "cap" in x]
recycling_files_cost = [x for x in recycling_files if "cap" not in x]
manufacturing_files = [x for x in files_list if x.startswith('Manufacturing')]
manufacturing_files_cap = [x for x in manufacturing_files if "cap" in x]
manufacturing_files_cost = [x for x in manufacturing_files if "cap" not in x]
manufacturing_files_cap
['Manufacturing_cap_NAICS_05', 'Manufacturing_cap_NAICS_1', 'Manufacturing_cap_NAICS_2', 'Manufacturing_cap_40209_05', 'Manufacturing_cap_40209_1', 'Manufacturing_cap_40209_2']
year_list = list(range(2025, 2051, 1))
number_year = list(range(1, 27, 1))
years_dict = dict(zip(number_year,year_list))
for files in recycling_files_cost:
testfile_path = glob.glob(os.path.join(cwd, "scenarios_26_years", files, 'plants.csv'))[0]
globals()['%s_plants' % (files)] = pd.read_csv(testfile_path)
globals()['%s_plants' % (files)] = globals()['%s_plants' % (files)].replace({"year": years_dict})
print('{}: {} locations'.format(files, len(globals()['%s_plants' % (files)]['location name'].unique())))
Recycling_NAICS_05: 81 locations Recycling_NAICS_1: 81 locations Recycling_NAICS_2: 81 locations Recycling_NAICS_5: 81 locations Recycling_NAICS_10: 81 locations Recycling_40209_05: 87 locations Recycling_40209_1: 81 locations Recycling_40209_2: 81 locations Recycling_40209_5: 81 locations Recycling_40209_10: 81 locations
Some of the manufacturing files have no solution, so we remove them:
manufacturing_files_cost
['Manufacturing_NAICS_0001', 'Manufacturing_NAICS_05', 'Manufacturing_NAICS_1', 'Manufacturing_NAICS_2', 'Manufacturing_40209_0001', 'Manufacturing_40209_05', 'Manufacturing_40209_1', 'Manufacturing_40209_2']
for files in manufacturing_files_cost:
testfile_path = glob.glob(os.path.join(cwd, "scenarios_26_years", files, 'plants.csv'))[0]
globals()['%s_plants' % (files)] = pd.read_csv(testfile_path)
globals()['%s_plants' % (files)] = globals()['%s_plants' % (files)].replace({"year": years_dict})
print('{}: {} locations'.format(files, len(globals()['%s_plants' % (files)]['location name'].unique())))
Manufacturing_NAICS_0001: 151 locations Manufacturing_NAICS_05: 62 locations Manufacturing_NAICS_1: 62 locations Manufacturing_NAICS_2: 62 locations Manufacturing_40209_0001: 143 locations Manufacturing_40209_05: 62 locations Manufacturing_40209_1: 62 locations Manufacturing_40209_2: 62 locations
for files in recycling_files_cap:
testfile_path = glob.glob(os.path.join(cwd, "scenarios_26_years", files, 'plants.csv'))[0]
globals()['%s_plants' % (files)] = pd.read_csv(testfile_path)
globals()['%s_plants' % (files)] = globals()['%s_plants' % (files)].replace({"year": years_dict})
print('{}: {} locations'.format(files, len(globals()['%s_plants' % (files)]['location name'].unique())))
Recycling_cap_NAICS_05: 161 locations Recycling_cap_NAICS_1: 81 locations Recycling_cap_NAICS_2: 41 locations Recycling_cap_NAICS_5: 17 locations Recycling_cap_NAICS_10: 9 locations Recycling_cap_40209_05: 161 locations Recycling_cap_40209_1: 81 locations Recycling_cap_40209_2: 43 locations Recycling_cap_40209_5: 17 locations Recycling_cap_40209_10: 10 locations
for files in manufacturing_files_cap:
testfile_path = glob.glob(os.path.join(cwd, "scenarios_26_years", files, 'plants.csv'))[0]
globals()['%s_plants' % (files)] = pd.read_csv(testfile_path)
globals()['%s_plants' % (files)] = globals()['%s_plants' % (files)].replace({"year": years_dict})
print('{}: {} locations'.format(files, len(globals()['%s_plants' % (files)]['location name'].unique())))
Manufacturing_cap_NAICS_05: 124 locations Manufacturing_cap_NAICS_1: 62 locations Manufacturing_cap_NAICS_2: 31 locations Manufacturing_cap_40209_05: 124 locations Manufacturing_cap_40209_1: 62 locations Manufacturing_cap_40209_2: 31 locations
rec_cap_locs_data = [['Capacity', 'Recycling', 'NAICS', 0.5, 161],
['Capacity', 'Recycling','NAICS',1, 81],
['Capacity', 'Recycling','NAICS',2, 41],
['Capacity', 'Recycling','NAICS',5, 17],
['Capacity', 'Recycling','NAICS',10, 9],
['Capacity', 'Recycling', '40209', 0.5, 161],
['Capacity', 'Recycling','40209',1, 81],
['Capacity', 'Recycling','40209',2, 41],
['Capacity', 'Recycling','40209',5, 17],
['Capacity', 'Recycling','40209',10, 10],
['Cost', 'Recycling', 'NAICS',0.5, 81],
['Cost', 'Recycling','NAICS',1, 81],
['Cost', 'Recycling','NAICS',2, 81],
['Cost', 'Recycling','NAICS',5, 81],
['Cost', 'Recycling','NAICS',10, 81],
['Cost', 'Recycling', '40209',0.5, 81],
['Cost', 'Recycling','40209',1, 81],
['Cost', 'Recycling','40209',2, 81],
['Cost', 'Recycling','40209',5, 81],
['Cost', 'Recycling','40209',10, 81],
['Capacity', 'Manufacturing', 'NAICS',0.5, 124],
['Capacity', 'Manufacturing', 'NAICS',1, 62],
['Capacity', 'Manufacturing', 'NAICS',2, 31],
['Capacity', 'Manufacturing', '40209', 0.5, 124],
['Capacity', 'Manufacturing', '40209', 1, 62],
['Capacity', 'Manufacturing', '40209', 2, 31],
['Cost', 'Manufacturing', 'NAICS',0.001, 151],
['Cost', 'Manufacturing', 'NAICS',0.5, 62],
['Cost', 'Manufacturing', 'NAICS',1, 62],
['Cost', 'Manufacturing', 'NAICS',2, 62],
['Cost', 'Manufacturing', '40209',0.001, 143],
['Cost', 'Manufacturing', '40209',0.5, 62],
['Cost', 'Manufacturing', '40209',1, 62],
['Cost', 'Manufacturing', '40209',2, 62]]
rec_cap_locs_data_df = pd.DataFrame(rec_cap_locs_data, columns=['Analysis', 'Facility', 'Location group','Factor', 'Selected locations'])
factors_rec = [0.5, 1, 2, 5, 10]
figure = sns.lineplot(x='Factor', y='Selected locations', data=rec_cap_locs_data_df.loc[rec_cap_locs_data_df['Facility'] == 'Recycling'], marker='o', style="Analysis", color=my_tiel)
figure.legend(frameon=False)
figure.set(title='Recycling', ylim = (0, 175))
plt.xticks(factors_rec, labels = factors_rec)
plt.savefig(os.path.join(cwd, f"recycling_selected.png"), transparent=True, dpi=300);
plt.savefig(os.path.join(cwd, f"recycling_selected.pdf"), dpi=300);
factors_man = [0.001, 0.5, 1, 2]
figure = sns.lineplot(x='Factor', y='Selected locations', data=rec_cap_locs_data_df.loc[rec_cap_locs_data_df['Facility'] == 'Manufacturing'], marker='o', style="Analysis", color=my_orange)
figure.legend(frameon=False)
figure.set(title='Manufacturing', ylim = (0, 175))
plt.xticks(factors_man, labels = factors_man)
plt.savefig(os.path.join(cwd, f"manufacturing_selected.png"), transparent=True, dpi=300);
plt.savefig(os.path.join(cwd, f"manufacturing_selected.pdf"), dpi=300);
for files in recycling_files_cap:
# Initialize a grid of plots with an Axes for each walk
grid = sns.FacetGrid(globals()['%s_plants' % (files)], col="location name",
col_wrap=9, height=1.5)
# Draw a horizontal line to show the starting point
grid.refline(y=0, linestyle=":")
grid.map(plt.plot, "year", "utilization factor (%)", color=my_tiel)
grid.map(plt.fill_between, 'year', 'utilization factor (%)',color= my_tiel, alpha= 0.2)
# Adjust the tick positions, labels and
#sns.set(font_scale=0.1)
grid.set_titles(row_template = '{row_name}', col_template = '{col_name}', size=5)
grid.fig.tight_layout(w_pad=1)
#grid.fig.subplots_adjust(top=0.9)
grid.fig.suptitle(f"{files}", y=1.2)
plt.savefig(os.path.join(cwd, f"grid_26_years/grid_{files}_cap_uf.png"), transparent=True, dpi=300);
plt.savefig(os.path.join(cwd, f"grid_26_years/grid_{files}_cap_uf.pdf"), dpi=300);
for files in recycling_files_cap:
# Initialize a grid of plots with an Axes for each walk
grid = sns.FacetGrid(globals()['%s_plants' % (files)], col="location name",
col_wrap=9, height=1.5)
# Draw a horizontal line to show the starting point
grid.refline(y=0, linestyle=":")
grid.map(plt.plot, "year", "total cost ($)", color=my_tiel)
grid.map(plt.fill_between, 'year', 'total cost ($)',color= my_tiel, alpha= 0.2)
# Adjust the tick positions, labels and
#sns.set(font_scale=0.1)
grid.set_titles(row_template = '{row_name}', col_template = '{col_name}', size=5)
grid.fig.tight_layout(w_pad=1)
grid.fig.subplots_adjust(top=0.9)
grid.fig.suptitle(f"{files}")
plt.savefig(os.path.join(cwd, f"grid_26_years/grid_{files}_cap_totcost.png"), transparent=True, dpi=300);
plt.savefig(os.path.join(cwd, f"grid_26_years/grid_{files}_cap_totcost.pdf"), dpi=300);
for files in recycling_files_cost:
# Initialize a grid of plots with an Axes for each walk
# Initialize a grid of plots with an Axes for each walk
grid = sns.FacetGrid(globals()['%s_plants' % (files)], col="location name",
col_wrap=9, height=1.5)
# Draw a horizontal line to show the starting point
grid.refline(y=0, linestyle=":")
grid.map(plt.plot, "year", "utilization factor (%)", color=my_tiel)
grid.map(plt.fill_between, "year", 'utilization factor (%)',color= my_tiel, alpha= 0.2)
# Adjust the tick positions, labels and
#sns.set(font_scale=0.1)
grid.set_titles(row_template = '{row_name}', col_template = '{col_name}', size=5)
grid.fig.tight_layout(w_pad=1)
grid.fig.subplots_adjust(top=0.9)
grid.fig.suptitle(f"{files}")
plt.savefig(os.path.join(cwd, f"grid_26_years/grid_{files}_cost_uf.png"), transparent=True, dpi=300);
plt.savefig(os.path.join(cwd, f"grid_26_years/grid_{files}_cost_uf.pdf"), dpi=300);
for files in recycling_files_cost:
# Initialize a grid of plots with an Axes for each walk
grid = sns.FacetGrid(globals()['%s_plants' % (files)], col="location name",
col_wrap=9, height=1.5)
# Draw a horizontal line to show the starting point
grid.refline(y=0, linestyle=":")
grid.map(plt.plot, "year", "total cost ($)", color=my_tiel)
grid.map(plt.fill_between, 'year', 'total cost ($)',color= my_tiel, alpha= 0.2)
# Adjust the tick positions, labels and
#sns.set(font_scale=0.1)
grid.set_titles(row_template = '{row_name}', col_template = '{col_name}', size=5)
grid.fig.tight_layout(w_pad=1)
grid.fig.subplots_adjust(top=0.9)
grid.fig.suptitle(f"{files}")
plt.savefig(os.path.join(cwd, f"grid_26_years/grid_{files}_cost_totcost.png"), transparent=True, dpi=300);
plt.savefig(os.path.join(cwd, f"grid_26_years/grid_{files}_cost_totcost.pdf"), dpi=300);
for files in manufacturing_files_cap:
# Initialize a grid of plots with an Axes for each walk
grid = sns.FacetGrid(globals()['%s_plants' % (files)], col="location name",
col_wrap=9, height=1.5)
# Draw a horizontal line to show the starting point
grid.refline(y=0, linestyle=":")
grid.map(plt.plot, "year", "utilization factor (%)", color=my_orange)
grid.map(plt.fill_between, 'year', 'utilization factor (%)',color= my_orange, alpha= 0.2)
# Adjust the tick positions, labels and
#sns.set(font_scale=0.1)
grid.set_titles(row_template = '{row_name}', col_template = '{col_name}', size=5)
grid.fig.tight_layout(w_pad=1)
grid.fig.subplots_adjust(top=0.9)
grid.fig.suptitle(f"{files}")
plt.savefig(os.path.join(cwd, f"grid_26_years/grid_{files}_cap_uf.png"), transparent=True, dpi=300);
plt.savefig(os.path.join(cwd, f"grid_26_years/grid_{files}_cap_uf.pdf"), dpi=300);
for files in manufacturing_files_cap:
# Initialize a grid of plots with an Axes for each walk
grid = sns.FacetGrid(globals()['%s_plants' % (files)], col="location name",
col_wrap=9, height=1.5)
# Draw a horizontal line to show the starting point
grid.refline(y=0, linestyle=":")
grid.map(plt.plot, "year", "total cost ($)", color=my_orange)
grid.map(plt.fill_between, 'year', 'total cost ($)',color= my_orange, alpha= 0.2)
# Adjust the tick positions, labels and
#sns.set(font_scale=0.1)
grid.set_titles(row_template = '{row_name}', col_template = '{col_name}', size=5)
grid.fig.tight_layout(w_pad=1)
grid.fig.subplots_adjust(top=0.9)
grid.fig.suptitle(f"{files}")
plt.savefig(os.path.join(cwd, f"grid_26_years/grid_{files}_cap_totcost.png"), transparent=True, dpi=300);
plt.savefig(os.path.join(cwd, f"grid_26_years/grid_{files}_cap_totcost.pdf"), dpi=300);
for files in manufacturing_files_cost:
# Initialize a grid of plots with an Axes for each walk
# Initialize a grid of plots with an Axes for each walk
grid = sns.FacetGrid(globals()['%s_plants' % (files)], col="location name",
col_wrap=9, height=1.5)
# Draw a horizontal line to show the starting point
grid.refline(y=0, linestyle=":")
grid.map(plt.plot, "year", "utilization factor (%)", color=my_orange)
grid.map(plt.fill_between, 'year', 'utilization factor (%)',color= my_orange, alpha= 0.2)
# Adjust the tick positions, labels and
#sns.set(font_scale=0.1)
grid.set_titles(row_template = '{row_name}', col_template = '{col_name}', size=5)
grid.fig.tight_layout(w_pad=1)
grid.fig.subplots_adjust(top=0.9)
grid.fig.suptitle(f"{files}")
plt.savefig(os.path.join(cwd, f"grid_26_years/grid_{files}_cost_uf.png"), transparent=True, dpi=300);
plt.savefig(os.path.join(cwd, f"grid_26_years/grid_{files}_cost_uf.pdf"), dpi=300);
for files in manufacturing_files_cost:
# Initialize a grid of plots with an Axes for each walk
grid = sns.FacetGrid(globals()['%s_plants' % (files)], col="location name",
col_wrap=9, height=1.5)
# Draw a horizontal line to show the starting point
grid.refline(y=0, linestyle=":")
grid.map(plt.plot, "year", "total cost ($)", color=my_orange)
grid.map(plt.fill_between, 'year', 'total cost ($)',color= my_orange, alpha= 0.2)
# Adjust the tick positions, labels and
#sns.set(font_scale=0.1)
grid.set_titles(row_template = '{row_name}', col_template = '{col_name}', size=5)
grid.fig.tight_layout(w_pad=1)
grid.fig.subplots_adjust(top=0.9)
grid.fig.suptitle(f"{files}")
plt.savefig(os.path.join(cwd, f"grid_26_years/grid_{files}_cost_totcost.png"), transparent=True, dpi=300);
plt.savefig(os.path.join(cwd, f"grid_26_years/grid_{files}_cost_totcost.pdf"));
USA map source2.
import fiona
import geopandas as gp
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib import collections
import matplotlib.pyplot as plt
from shapely.geometry import LineString, Point, Polygon
%matplotlib inline
for files in recycling_files_cost:
testfile_path = glob.glob(os.path.join(cwd, "scenarios_26_years", files, 'transportation.csv'))[0]
globals()['%s_transportation' % (files)] = pd.read_csv(testfile_path)
globals()['%s_transportation' % (files)] = globals()['%s_transportation' % (files)].replace({"year": years_dict})
for files in recycling_files_cap:
testfile_path = glob.glob(os.path.join(cwd, "scenarios_26_years", files, 'transportation.csv'))[0]
globals()['%s_transportation' % (files)] = pd.read_csv(testfile_path)
globals()['%s_transportation' % (files)] = globals()['%s_transportation' % (files)].replace({"year": years_dict})
for files in manufacturing_files_cost:
testfile_path = glob.glob(os.path.join(cwd, "scenarios_26_years", files, 'transportation.csv'))[0]
globals()['%s_transportation' % (files)] = pd.read_csv(testfile_path)
globals()['%s_transportation' % (files)] = globals()['%s_transportation' % (files)].replace({"year": years_dict})
for files in manufacturing_files_cap:
testfile_path = glob.glob(os.path.join(cwd, "scenarios_26_years", files, 'transportation.csv'))[0]
globals()['%s_transportation' % (files)] = pd.read_csv(testfile_path)
globals()['%s_transportation' % (files)] = globals()['%s_transportation' % (files)].replace({"year": years_dict})
my_tiel = (0.4, 0.7607843137254902, 0.6470588235294118)
for files in recycling_files_cost:
# Plot base map
world = gp.read_file(os.path.join(cwd, f'resources','USA_States_(Generalized)', 'USA_States_Generalized.shp'))
world = world.to_crs("EPSG:4326")
ax = world.plot(color="0.9", edgecolor="1", figsize=(14, 7))
ax.set_ylim([23, 50])
ax.set_xlim([-128, -65])
plt.axis('off')
ax.set(title=f'{files}')
# Draw transportation lines
data = globals()['%s_transportation' % (files)]
lines = [
[
(
row["source longitude (deg)"],
row["source latitude (deg)"],
),
(
row["destination longitude (deg)"],
row["destination latitude (deg)"],
),
]
for (index, row) in data.iterrows()
]
ax.add_collection(
collections.LineCollection(
lines,
linewidths=0.005,
zorder=1,
alpha=1,
color=my_tiel,
)
)
# Draw source points
points = gp.points_from_xy(
data["source longitude (deg)"],
data["source latitude (deg)"],
)
gp.GeoDataFrame(data, geometry=points).plot(ax=ax, marker='D',color=my_tiel, markersize=10, edgecolor='white', linewidth=0.5)
# Draw destination points
points = gp.points_from_xy(
data["destination longitude (deg)"],
data["destination latitude (deg)"],
)
gp.GeoDataFrame(data, geometry=points).plot(ax=ax, markersize=60, color=my_tiel , edgecolor='white', linewidth=0.5)
legend_elements = [Line2D([0], [0], color=my_tiel, lw=1, label='Transport route'),
Line2D([0], [0], marker='D', color=my_tiel, label='Collection center', markersize=3, linestyle='None'),
Line2D([0], [0], marker='o', color=my_tiel, label='Recycling center', markersize=10, linestyle='None')]
ax.legend(handles=legend_elements, frameon=False, bbox_to_anchor=(0.35, 0.25))
plt.savefig(os.path.join(cwd, "maps_26_years",f"map_{files}_cost.png"), transparent=True, dpi=300);
plt.savefig(os.path.join(cwd, "maps_26_years",f"map_{files}_cost.pdf"), dpi=300);
for files in recycling_files_cap:
# Plot base map
world = gp.read_file(os.path.join(cwd, f'resources','USA_States_(Generalized)', 'USA_States_Generalized.shp'))
world = world.to_crs("EPSG:4326")
ax = world.plot(color="0.9", edgecolor="1", figsize=(14, 7))
ax.set_ylim([23, 50])
ax.set_xlim([-128, -65])
plt.axis('off')
ax.set(title=f'{files}')
# Draw transportation lines
data = globals()['%s_transportation' % (files)]
lines = [
[
(
row["source longitude (deg)"],
row["source latitude (deg)"],
),
(
row["destination longitude (deg)"],
row["destination latitude (deg)"],
),
]
for (index, row) in data.iterrows()
]
ax.add_collection(
collections.LineCollection(
lines,
linewidths=0.005,
zorder=1,
alpha=1,
color=my_tiel,
)
)
# Draw source points
points = gp.points_from_xy(
data["source longitude (deg)"],
data["source latitude (deg)"],
)
gp.GeoDataFrame(data, geometry=points).plot(ax=ax, marker='D',color=my_tiel, markersize=10, edgecolor='white', linewidth=0.5)
# Draw destination points
points = gp.points_from_xy(
data["destination longitude (deg)"],
data["destination latitude (deg)"],
)
gp.GeoDataFrame(data, geometry=points).plot(ax=ax, markersize=60, color=my_tiel, edgecolor='white', linewidth=0.5)
legend_elements = [Line2D([0], [0], color=my_tiel, lw=1, label='Transport route'),
Line2D([0], [0], marker='D', color=my_tiel, label='Collection center', markersize=3, linestyle='None'),
Line2D([0], [0], marker='o', color=my_tiel, label='Recycling center', markersize=10, linestyle='None')]
ax.legend(handles=legend_elements, frameon=False, bbox_to_anchor=(0.35, 0.25))
plt.savefig(os.path.join(cwd, "maps_26_years",f"map_{files}_cap.png"), transparent=True, dpi=300);
plt.savefig(os.path.join(cwd, "maps_26_years",f"map_{files}_cap.pdf"), dpi=300);
for files in manufacturing_files_cost:
# Plot base map
world = gp.read_file(os.path.join(cwd, 'resources','USA_States_(Generalized)', 'USA_States_Generalized.shp'))
world = world.to_crs("EPSG:4326")
ax = world.plot(color="0.9", edgecolor="1", figsize=(14, 7))
ax.set_ylim([23, 50])
ax.set_xlim([-128, -65])
plt.axis('off')
ax.set(title=f'{files}')
# Draw transportation lines
data = globals()['%s_transportation' % (files)]
lines = [
[
(
row["source longitude (deg)"],
row["source latitude (deg)"],
),
(
row["destination longitude (deg)"],
row["destination latitude (deg)"],
),
]
for (index, row) in data.iterrows()
]
ax.add_collection(
collections.LineCollection(
lines,
linewidths=0.005,
zorder=1,
#alpha=0.3,
color=my_orange,
)
)
# Draw source points
points = gp.points_from_xy(
data["source longitude (deg)"],
data["source latitude (deg)"],
)
gp.GeoDataFrame(data, geometry=points).plot(ax=ax, marker='D',color=my_orange, markersize=10, edgecolor='white', linewidth=0.5)
# Draw destination points
points = gp.points_from_xy(
data["destination longitude (deg)"],
data["destination latitude (deg)"],
)
gp.GeoDataFrame(data, geometry=points).plot(ax=ax,markersize=60, color=my_orange, edgecolor='white', linewidth=0.5)
legend_elements = [Line2D([0], [0], color=my_orange, lw=1, label='Transport route'),
Line2D([0], [0], marker='D', color=my_orange, label='Collection center', markersize=3, linestyle='None'),
Line2D([0], [0], marker='o', color=my_orange, label='Recycling center', markersize=10, linestyle='None')]
ax.legend(handles=legend_elements, frameon=False, bbox_to_anchor=(0.35, 0.25))
plt.savefig(os.path.join(cwd, "maps_26_years",f"map_{files}_cost.png"), transparent=True, dpi=300);
plt.savefig(os.path.join(cwd, "maps_26_years",f"map_{files}_cost.pdf"), dpi=300);
for files in manufacturing_files_cap:
# Plot base map
world = gp.read_file(os.path.join(f'resources','USA_States_(Generalized)', 'USA_States_Generalized.shp'))
world = world.to_crs("EPSG:4326")
ax = world.plot(color="0.9", edgecolor="1", figsize=(14, 7))
ax.set_ylim([23, 50])
ax.set_xlim([-128, -65])
plt.axis('off')
ax.set(title=f'{files}')
# Draw transportation lines
data = globals()['%s_transportation' % (files)]
lines = [
[
(
row["source longitude (deg)"],
row["source latitude (deg)"],
),
(
row["destination longitude (deg)"],
row["destination latitude (deg)"],
),
]
for (index, row) in data.iterrows()
]
ax.add_collection(
collections.LineCollection(
lines,
linewidths=0.005,
zorder=1,
#alpha=0.3,
color=my_orange,
)
)
# Draw source points
points = gp.points_from_xy(
data["source longitude (deg)"],
data["source latitude (deg)"],
)
gp.GeoDataFrame(data, geometry=points).plot(ax=ax, marker='D',color=my_orange, markersize=10, edgecolor='white', linewidth=0.5)
# Draw destination points
points = gp.points_from_xy(
data["destination longitude (deg)"],
data["destination latitude (deg)"],
)
gp.GeoDataFrame(data, geometry=points).plot(ax=ax, markersize=60, color=my_orange, edgecolor='white', linewidth=0.5)
legend_elements = [Line2D([0], [0], color=my_orange, lw=1, label='Transport route'),
Line2D([0], [0], marker='D', color=my_orange, label='Collection center', markersize=3, linestyle='None'),
Line2D([0], [0], marker='o', color=my_orange, label='Recycling center', markersize=10, linestyle='None')]
ax.legend(handles=legend_elements, frameon=False, bbox_to_anchor=(0.35, 0.25))
plt.savefig(os.path.join(cwd, "maps_26_years",f"map_{files}_cap.png"), transparent=True, dpi=300);
plt.savefig(os.path.join(cwd, "maps_26_years",f"map_{files}_cap.pdf"), dpi=300);
def intersection(lst1, lst2):
lst3 = [value for value in lst1 if value in lst2]
return lst3
print('40209 overlap x0.5 cost:', len(intersection(Recycling_40209_05_plants['location name'].unique(),
Manufacturing_40209_05_plants['location name'].unique())))
print('40209 overlap x1 cost:', len(intersection(Recycling_40209_1_plants['location name'].unique(),
Manufacturing_40209_1_plants['location name'].unique())))
print('40209 overlap x2 cost:', len(intersection(Recycling_40209_2_plants['location name'].unique(),
Manufacturing_40209_2_plants['location name'].unique())))
print('NAICS overlap x0.5 cost:', len(intersection(Recycling_NAICS_05_plants['location name'].unique(),
Manufacturing_NAICS_05_plants['location name'].unique())))
print('NAICS overlap x1 cost:', len(intersection(Recycling_NAICS_1_plants['location name'].unique(),
Manufacturing_NAICS_1_plants['location name'].unique())))
print('NAICS overlap x2 cost:', len(intersection(Recycling_NAICS_2_plants['location name'].unique(),
Manufacturing_NAICS_2_plants['location name'].unique())))
print('40209 overlap x1 capacity:', len(intersection(Recycling_cap_40209_1_plants['location name'].unique(),
Manufacturing_cap_40209_1_plants['location name'].unique())))
print('40209 overlap x2 capacity:', len(intersection(Recycling_cap_40209_2_plants['location name'].unique(),
Manufacturing_cap_40209_2_plants['location name'].unique())))
print('NAICS overlap x1 capacity:', len(intersection(Recycling_cap_NAICS_1_plants['location name'].unique(),
Manufacturing_cap_NAICS_1_plants['location name'].unique()))),
print('NAICS overlap x2 capacity:', len(intersection(Recycling_cap_NAICS_2_plants['location name'].unique(),
Manufacturing_cap_NAICS_2_plants['location name'].unique())))
40209 overlap x0.5 cost: 55 40209 overlap x1 cost: 60 40209 overlap x2 cost: 62 NAICS overlap x0.5 cost: 46 NAICS overlap x1 cost: 54 NAICS overlap x2 cost: 60 40209 overlap x1 capacity: 60 40209 overlap x2 capacity: 25 NAICS overlap x1 capacity: 54 NAICS overlap x2 capacity: 25
Recycling_40209_1_plants.keys()
Index(['plant type', 'location name', 'year', 'latitude (deg)',
'longitude (deg)', 'capacity (tonne)', 'amount processed (tonne)',
'amount received (tonne)', 'amount in storage (tonne)',
'utilization factor (%)', 'energy (GJ)', 'opening cost ($)',
'expansion cost ($)', 'fixed operating cost ($)',
'variable operating cost ($)', 'storage cost ($)', 'total cost ($)'],
dtype='object')
common_rec_40209 = intersection( # Cost analysis
intersection(
intersection(
intersection(
Recycling_40209_1_plants['location name'].unique(),
Recycling_40209_05_plants['location name'].unique()),
Recycling_40209_2_plants['location name'].unique()),
Recycling_40209_5_plants['location name'].unique()),
Recycling_40209_10_plants['location name'].unique())
len(common_rec_40209)
59
common_rec_cap_40209 = intersection( # Capacity analysis
intersection(
intersection(
intersection(
Recycling_cap_40209_1_plants['location name'].unique(),
Recycling_cap_40209_05_plants['location name'].unique()),
Recycling_cap_40209_2_plants['location name'].unique()),
Recycling_cap_40209_5_plants['location name'].unique()),
Recycling_cap_40209_10_plants['location name'].unique())
len(common_rec_cap_40209)
9
Recycling_cap_40209_05_plants['location name']
0 Glen Lyn, Virginia
1 Glen Lyn, Virginia
2 Glen Lyn, Virginia
3 Glen Lyn, Virginia
4 Glen Lyn, Virginia
...
4181 J C Weadock, Michigan
4182 J C Weadock, Michigan
4183 J C Weadock, Michigan
4184 J C Weadock, Michigan
4185 J C Weadock, Michigan
Name: location name, Length: 4186, dtype: object
common_man_40209 = intersection( # Cost analysis
intersection(
intersection(
Manufacturing_40209_0001_plants['location name'].unique(),
Manufacturing_40209_05_plants['location name'].unique()),
Manufacturing_40209_1_plants['location name'].unique()),
Manufacturing_40209_2_plants['location name'].unique())
len(common_man_40209)
48
common_man_cap_40209 = intersection( # Capacity analysis
Manufacturing_cap_40209_1_plants['location name'].unique(),
Manufacturing_cap_40209_2_plants['location name'].unique())
len(common_man_cap_40209)
31
common_40209 = intersection(
intersection(
intersection(
common_rec_40209, common_rec_cap_40209),
common_man_40209),
common_man_cap_40209)
len(common_40209)
6
common_40209
['Cholla, Arizona', 'R D Morrow, Mississippi', 'Northeastern, Oklahoma', 'Crystal River, Florida', 'Potomac River, Virginia', 'Navajo, Arizona']
I choose three locations:
- 'Navajo, Arizona'
- 'Clinch River, Virginia'
- 'R D Morrow, Mississippi'common_40209_selected = ['Navajo, Arizona', 'Clinch River, Virginia', 'R D Morrow, Mississippi']
common_rec_NAICS = intersection( # Cost analysis
intersection(
intersection(
intersection(
Recycling_NAICS_1_plants['location name'].unique(),
Recycling_NAICS_05_plants['location name'].unique()),
Recycling_NAICS_2_plants['location name'].unique()),
Recycling_NAICS_5_plants['location name'].unique()),
Recycling_NAICS_10_plants['location name'].unique())
len(common_rec_NAICS)
52
common_rec_cap_NAICS = intersection( # Capacity analysis
intersection(
intersection(
intersection(
Recycling_cap_NAICS_1_plants['location name'].unique(),
Recycling_cap_NAICS_05_plants['location name'].unique()),
Recycling_cap_NAICS_2_plants['location name'].unique()),
Recycling_cap_NAICS_5_plants['location name'].unique()),
Recycling_cap_NAICS_10_plants['location name'].unique())
len(common_rec_cap_NAICS)
9
common_man_NAICS = intersection( # Cost analysis
intersection(
intersection(
Manufacturing_NAICS_0001_plants['location name'].unique(),
Manufacturing_NAICS_05_plants['location name'].unique()),
Manufacturing_NAICS_1_plants['location name'].unique()),
Manufacturing_NAICS_2_plants['location name'].unique())
len(common_man_NAICS)
48
common_man_cap_NAICS = intersection( # Capacity analysis
Manufacturing_cap_NAICS_1_plants['location name'].unique(),
Manufacturing_cap_NAICS_2_plants['location name'].unique())
len(common_man_cap_NAICS)
31
common_NAICS = intersection(
intersection(
intersection(
common_rec_NAICS, common_rec_cap_NAICS),
common_man_NAICS),
common_man_cap_NAICS)
len(common_NAICS)
5
common_NAICS
['Baldor Electric Co., AR', 'Standard Enterprises, Inc., VA', 'MMC Materials, Inc., MS', 'Boye Knives, AZ', 'Bain Mfg. Co., Inc., MS']
The corresponding counties here are:
- Clarksville, Arizona
- Columbia, Missisipi
- Lake Havasu City, Arizona
- Charlottesville, Virginia
- Phoenix, Arizona
- Madison County, Missisipi
- Dolan Springs, Arizona
- Portsmouth, Virginia
- Grenada, MissisipiSelected:
- Dolan Springs, Arizona or 'Boye Knives, AZ', close to Hualapai reservation.
- Grenada, Missisipi or 'Bain Mfg. Co., Inc., MS': Close to a highway,
- Charlottesville, Virginia or 'Standard Enterprises, Inc.': More remote, plt.rcParams['axes.prop_cycle'] = plt.cycler(color=plt.cm.Set2.colors[:6])
years_list = np.arange(2025, 2051, 5)
fig, axs = plt.subplots(3, 2, figsize=(10,10),sharex=True)
fig.tight_layout(pad=1.5)
fig.suptitle("40209 Recycling plants - Cost Sensitivity Analysis",fontsize=20)
fig.subplots_adjust(top=0.92)
for files in recycling_files_cost:
if '40209' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Navajo, Arizona']
regex = re.findall('\w+_\w+_(\d{1,2})', files)[0]
axs[0, 0].plot(state['year'], state['utilization factor (%)'], label = fr'{regex}')
axs[0, 0].fill_between(state['year'], state['utilization factor (%)'], alpha=0.1)
#axs[0].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[0, 0].set_title('Navajo, Arizona')
axs[0, 0].margins(x=0)
# axs[0].xlabel('Year')
# axs[0].ylabel('Utilization factor (%)')
axs[0, 0].set(xlabel=" ", ylabel="Utilization factor (%)")
for files in recycling_files_cost:
if '40209' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'R D Morrow, Mississippi']
regex = re.findall('\w+_\w+_(\d{1,2})', files)[0]
axs[1, 0].plot(state['year'], state['utilization factor (%)'], label = fr'{regex}')
axs[1, 0].fill_between(state['year'], state['utilization factor (%)'], alpha=0.1)
#axs[2].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[1, 0].set_title('R D Morrow, Mississippi')
axs[1, 0].margins(x=0)
axs[1, 0].set(xlabel=" ", ylabel="Utilization factor (%)")
for files in recycling_files_cost:
if '40209' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Clinch River, Virginia']
regex = re.findall('\w+_\w+_(\d{1,2})', files)[0]
axs[2, 0].plot(state['year'], state['utilization factor (%)'], label = fr'{regex}')
axs[2, 0].fill_between(state['year'], state['utilization factor (%)'], alpha=0.1)
#axs[1, 0].legend(bbox_to_anchor=(1.1, 0.75), frameon=False)
axs[2, 0].set_title('Clinch River, Virginia')
axs[2, 0].margins(x=0)
axs[2, 0].set(xlabel=" ", ylabel="Utilization factor (%)")
axs[2, 0].set(xlabel="Year", ylabel="Utilization factor (%)", ylim = (0, 105))
axs[2, 0].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor')
for files in recycling_files_cost:
if '40209' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Navajo, Arizona']
regex = re.findall('\w+_\w+_(\d{1,2})', files)[0]
axs[0, 1].plot(state['year'], state['total cost ($)'], label = fr'{regex}')
axs[0, 1].fill_between(state['year'], state['total cost ($)'], alpha=0.1)
#axs[0].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[0, 1].set_title('Navajo, Arizona')
axs[0, 1].margins(x=0)
# axs[0].xlabel('Year')
# axs[0].ylabel('Utilization factor (%)')
axs[0, 1].set(xlabel=" ", ylabel="Total cost ($)", ylim = (0, 125000000))
for files in recycling_files_cost:
if '40209' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'R D Morrow, Mississippi']
regex = re.findall('\w+_\w+_(\d{1,2})', files)[0]
axs[1, 1].plot(state['year'], state['total cost ($)'], label = fr'{regex}')
axs[1, 1].fill_between(state['year'], state['total cost ($)'], alpha=0.1)
axs[1, 1].legend(bbox_to_anchor=(1.2, 0.75), frameon=False)
axs[1, 1].set_title('R D Morrow, Mississippi')
axs[1, 1].margins(x=0, y=5)
axs[1, 1].set(xlabel=" ", ylabel="Total cost ($)", ylim = (0, 125000000))
for files in recycling_files_cost:
if '40209' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Clinch River, Virginia']
regex = re.findall('\w+_\w+_(\d{1,2})', files)[0]
axs[2, 1].plot(state['year'], state['total cost ($)'], label = fr'{regex}')
axs[2, 1].fill_between(state['year'], state['total cost ($)'], alpha=0.1)
axs[2, 1].set_title('Clinch River, Virginia')
axs[2, 1].margins(x=0)
axs[2, 1].set(xlabel="Year", ylabel="Total cost ($)", ylim = (0, 125000000))
axs[2, 1].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor')
plt.gcf().subplots_adjust(bottom=0.06, right=0.91, left=0.06)
fig.savefig(os.path.join(cwd, f"selected_plants_26_years/Recycling_40209_cost_analysis.png"), dpi=300, transparent=True);
fig.savefig(os.path.join(cwd, f"selected_plants_26_years/Recycling_40209_cost_analysis.pdf"), dpi=300);
/var/folders/w2/85_h_nyn0mlbxf81x3k9n53xh90n_1/T/ipykernel_99819/2293070169.py:41: UserWarning: FixedFormatter should only be used together with FixedLocator axs[2, 0].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor') /var/folders/w2/85_h_nyn0mlbxf81x3k9n53xh90n_1/T/ipykernel_99819/2293070169.py:76: UserWarning: FixedFormatter should only be used together with FixedLocator axs[2, 1].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor')
fig, axs = plt.subplots(3, 2, figsize=(10,10),sharex=True)
fig.tight_layout(pad=1.5)
fig.suptitle("40209 Recycling plants - Capacity Sensitivity Analysis",fontsize=20)
fig.subplots_adjust(top=0.92)
for files in recycling_files_cap:
if '40209' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Navajo, Arizona']
regex = re.findall('\w+_\w+_(\d{1,2})', files)[0]
axs[0, 0].plot(state['year'], state['utilization factor (%)'], label = fr'{regex}')
axs[0, 0].fill_between(state['year'], state['utilization factor (%)'], alpha=0.1)
#axs[0].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[0, 0].set_title('Navajo, Arizona')
axs[0, 0].margins(x=0)
# axs[0].xlabel('Year')
# axs[0].ylabel('Utilization factor (%)')
axs[0, 0].set(xlabel=" ", ylabel="Utilization factor (%)")
for files in recycling_files_cap:
if '40209' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'R D Morrow, Mississippi']
regex = re.findall('\w+_\w+_(\d{1,2})', files)[0]
axs[1, 0].plot(state['year'], state['utilization factor (%)'], label = fr'{regex}')
axs[1, 0].fill_between(state['year'], state['utilization factor (%)'], alpha=0.1)
#axs[2].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[1, 0].set_title('R D Morrow, Mississippi')
axs[1, 0].set(xlabel=" ", ylabel="Utilization factor (%)",ylim = (0, 105))
for files in recycling_files_cap:
if '40209' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Clinch River, Virginia']
regex = re.findall('\w+_\w+_(\d{1,2})', files)[0]
axs[2, 0].plot(state['year'], state['utilization factor (%)'], label = fr'{regex}')
axs[2, 0].fill_between(state['year'], state['utilization factor (%)'], alpha=0.1)
#axs[1, 0].legend(bbox_to_anchor=(1.1, 0.75), frameon=False)
axs[2, 0].set_title('Clinch River, Virginia')
axs[2, 0].margins(x=0)
axs[2, 0].set(xlabel="Year", ylabel="Utilization factor (%)", ylim = (0, 105))
axs[2, 0].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor')
for files in recycling_files_cap:
if '40209' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Navajo, Arizona']
regex = re.findall('\w+_\w+_(\d{1,2})', files)[0]
axs[0, 1].plot(state['year'], state['total cost ($)'], label = fr'{regex}')
axs[0, 1].fill_between(state['year'], state['total cost ($)'], alpha=0.1)
#axs[0].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[0, 1].set_title('Navajo, Arizona')
axs[0, 1].set(xlabel=" ", ylabel="Total cost ($)", ylim = (0, 26200000))
for files in recycling_files_cap:
if '40209' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'R D Morrow, Mississippi']
regex = re.findall('\w+_\w+_(\d{1,2})', files)[0]
axs[1, 1].plot(state['year'], state['total cost ($)'], label = fr'{regex}')
axs[1, 1].fill_between(state['year'], state['total cost ($)'], alpha=0.1)
axs[1, 1].legend(bbox_to_anchor=(1.2, 0.75), frameon=False)
axs[1, 1].set_title('R D Morrow, Mississippi')
axs[1, 1].margins(x=0)
axs[1, 1].set(xlabel=" ", ylabel="Total cost ($)", ylim = (0, 26200000))
for files in recycling_files_cap:
if '40209' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Clinch River, Virginia']
regex = re.findall('\w+_\w+_(\d{1,2})', files)[0]
axs[2, 1].plot(state['year'], state['total cost ($)'], label = fr'{regex}')
axs[2, 1].fill_between(state['year'], state['total cost ($)'], alpha=0.1)
axs[2, 1].set_title('Clinch River, Virginia')
axs[2, 1].margins(x=0)
axs[2, 1].set(xlabel="Year", ylabel="Total cost ($)", ylim = (0, 26200000))
axs[2, 1].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor')
plt.gcf().subplots_adjust(bottom=0.06, right=0.91, left=0.06)
fig.savefig(os.path.join(cwd, f"selected_plants_26_years/Recycling_40209_capacity_analysis.png"), dpi=300, transparent=True);
fig.savefig(os.path.join(cwd, f"selected_plants_26_years/Recycling_40209_capacity_analysis.pdf"), dpi=300);
/var/folders/w2/85_h_nyn0mlbxf81x3k9n53xh90n_1/T/ipykernel_99819/3209683946.py:39: UserWarning: FixedFormatter should only be used together with FixedLocator axs[2, 0].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor') /var/folders/w2/85_h_nyn0mlbxf81x3k9n53xh90n_1/T/ipykernel_99819/3209683946.py:71: UserWarning: FixedFormatter should only be used together with FixedLocator axs[2, 1].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor')
fig, axs = plt.subplots(3, 2, figsize=(10,10),sharex=True)
fig.tight_layout(pad=1.5)
fig.suptitle("NAICS Recycling plants - Cost Sensitivity Analysis",fontsize=20)
fig.subplots_adjust(top=0.92)
for files in recycling_files_cost:
if 'NAICS' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Boye Knives, AZ']
regex = re.findall('\w+_\w+_(\d{1,2})', files)[0]
axs[0, 0].plot(state['year'], state['utilization factor (%)'], label = fr'{regex}')
axs[0, 0].fill_between(state['year'], state['utilization factor (%)'], alpha=0.1)
#axs[0].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[0, 0].set_title('Dolan Springs, Arizona')
axs[0, 0].margins(x=0)
# axs[0].xlabel('Year')
# axs[0].ylabel('Utilization factor (%)')
axs[0, 0].set(xlabel=" ", ylabel="Utilization factor (%)")
for files in recycling_files_cost:
if 'NAICS' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Bain Mfg. Co., Inc., MS']
regex = re.findall('\w+_\w+_(\d{1,2})', files)[0]
axs[1, 0].plot(state['year'], state['utilization factor (%)'], label = fr'{regex}')
axs[1, 0].fill_between(state['year'], state['utilization factor (%)'], alpha=0.1)
#axs[1, 0].legend(bbox_to_anchor=(1.1, 0.75), frameon=False)
axs[1, 0].set_title('Grenada, Missisipi')
axs[1, 0].margins(x=0)
axs[1, 0].set(xlabel=" ", ylabel="Utilization factor (%)")
# for ax in axs.flat:
# ax.set(xlabel='Year', ylabel='Utilization factor (%)')
for files in recycling_files_cost:
if 'NAICS' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Standard Enterprises, Inc., VA']
regex = re.findall('\w+_\w+_(\d{1,2})', files)[0]
axs[2, 0].plot(state['year'], state['utilization factor (%)'], label = fr'{regex}')
axs[2, 0].fill_between(state['year'], state['utilization factor (%)'], alpha=0.1)
#axs[2].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[2, 0].set_title('Charlottesville, Virginia')
axs[2, 0].margins(x=0)
axs[2, 0].set(xlabel="Year", ylabel="Utilization factor (%)")
axs[2, 0].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor')
for files in recycling_files_cost:
if 'NAICS' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Boye Knives, AZ']
regex = re.findall('\w+_\w+_(\d{1,2})', files)[0]
axs[0, 1].plot(state['year'], state['total cost ($)'], label = fr'{regex}')
axs[0, 1].fill_between(state['year'], state['total cost ($)'], alpha=0.1)
#axs[0].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[0, 1].set_title('Dolan Springs, Arizona')
axs[0, 1].margins(x=0)
# axs[0].xlabel('Year')
# axs[0].ylabel('Utilization factor (%)')
axs[0, 1].set(xlabel=" ", ylabel="Total cost ($)", ylim = (0, 125000000))
for files in recycling_files_cost:
if 'NAICS' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Bain Mfg. Co., Inc., MS']
regex = re.findall('\w+_\w+_(\d{1,2})', files)[0]
axs[1, 1].plot(state['year'], state['total cost ($)'], label = fr'{regex}')
axs[1, 1].fill_between(state['year'], state['total cost ($)'], alpha=0.1)
axs[1, 1].legend(bbox_to_anchor=(1.2, 0.75), frameon=False)
axs[1, 1].set_title('Grenada, Missisipi')
axs[1, 1].margins(x=0)
axs[1, 1].set(xlabel=" ", ylabel="Total cost ($)", ylim = (0, 125000000))
for files in recycling_files_cost:
if 'NAICS' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Standard Enterprises, Inc., VA']
regex = re.findall('\w+_\w+_(\d{1,2})', files)[0]
axs[2, 1].plot(state['year'], state['total cost ($)'], label = fr'{regex}')
axs[2, 1].fill_between(state['year'], state['total cost ($)'], alpha=0.1)
#axs[2].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[2, 1].set_title('Charlottesville, Virginia')
axs[2, 1].margins(x=0, y=5)
axs[2, 1].set(xlabel="Year", ylabel="Total cost ($)", ylim = (0, 125000000))
axs[2, 1].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor')
plt.gcf().subplots_adjust(bottom=0.06, right=0.91, left=0.06)
fig.savefig(os.path.join(cwd, f"selected_plants_26_years","Recycling_NAICS_cost_analysis.png"), dpi=300, transparent=True);
fig.savefig(os.path.join(cwd, f"selected_plants_26_years","Recycling_NAICS_cost_analysis.pdf"), dpi=300);
/var/folders/w2/85_h_nyn0mlbxf81x3k9n53xh90n_1/T/ipykernel_99819/2907320501.py:42: UserWarning: FixedFormatter should only be used together with FixedLocator axs[2, 0].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor') /var/folders/w2/85_h_nyn0mlbxf81x3k9n53xh90n_1/T/ipykernel_99819/2907320501.py:78: UserWarning: FixedFormatter should only be used together with FixedLocator axs[2, 1].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor')
fig, axs = plt.subplots(3, 2, figsize=(10,10),sharex=True)
fig.tight_layout(pad=1.5)
fig.suptitle("NAICS Recycling plants - Capacity Sensitivity Analysis",fontsize=20)
fig.subplots_adjust(top=0.92)
for files in recycling_files_cap:
if 'NAICS' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Boye Knives, AZ']
regex = re.findall('\w+_\w+_(\d{1,2})', files)[0]
axs[0, 0].plot(state['year'], state['utilization factor (%)'], label = fr'{regex}')
axs[0, 0].fill_between(state['year'], state['utilization factor (%)'], alpha=0.1)
#axs[0].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[0, 0].set_title('Dolan Springs, Arizona')
axs[0, 0].margins(x=0)
# axs[0].xlabel('Year')
# axs[0].ylabel('Utilization factor (%)')
axs[0, 0].set(xlabel=" ", ylabel="Utilization factor (%)")
for files in recycling_files_cap:
if 'NAICS' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Bain Mfg. Co., Inc., MS']
regex = re.findall('\w+_\w+_(\d{1,2})', files)[0]
axs[1, 0].plot(state['year'], state['utilization factor (%)'], label = fr'{regex}')
axs[1, 0].fill_between(state['year'], state['utilization factor (%)'], alpha=0.1)
#axs[1, 0].legend(bbox_to_anchor=(1.1, 0.75), frameon=False)
axs[1, 0].set_title('Grenada, Missisipi')
axs[1, 0].margins(x=0)
axs[1, 0].set(xlabel=" ", ylabel="Utilization factor (%)")
# for ax in axs.flat:
# ax.set(xlabel='Year', ylabel='Utilization factor (%)')
for files in recycling_files_cap:
if 'NAICS' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Standard Enterprises, Inc., VA']
regex = re.findall('\w+_\w+_(\d{1,2})', files)[0]
axs[2, 0].plot(state['year'], state['utilization factor (%)'], label = fr'{regex}')
axs[2, 0].fill_between(state['year'], state['utilization factor (%)'], alpha=0.1)
#axs[2].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[2, 0].set_title('Charlottesville, Virginia')
axs[2, 0].margins(x=0)
axs[2, 0].set(xlabel="Year", ylabel="Utilization factor (%)")
axs[2, 0].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor')
for files in recycling_files_cap:
if 'NAICS' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Boye Knives, AZ']
regex = re.findall('\w+_\w+_(\d{1,2})', files)[0]
axs[0, 1].plot(state['year'], state['total cost ($)'], label = fr'{regex}')
axs[0, 1].fill_between(state['year'], state['total cost ($)'], alpha=0.1)
#axs[0].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[0, 1].set_title('Dolan Springs, Arizona')
axs[0, 1].margins(x=0)
# axs[0].xlabel('Year')
# axs[0].ylabel('Utilization factor (%)')
axs[0, 1].set(xlabel=" ", ylabel="Total cost ($)", ylim = (0, 26200000))
for files in recycling_files_cap:
if 'NAICS' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Bain Mfg. Co., Inc., MS']
regex = re.findall('\w+_\w+_(\d{1,2})', files)[0]
axs[1, 1].plot(state['year'], state['total cost ($)'], label = fr'{regex}')
axs[1, 1].fill_between(state['year'], state['total cost ($)'], alpha=0.1)
axs[1, 1].legend(bbox_to_anchor=(1.2, 0.75), frameon=False)
axs[1, 1].set_title('Grenada, Missisipi')
axs[1, 1].margins(x=0)
axs[1, 1].set(xlabel=" ", ylabel="Total cost ($)", ylim = (0, 26200000))
for files in recycling_files_cap:
if 'NAICS' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Standard Enterprises, Inc., VA']
regex = re.findall('\w+_\w+_(\d{1,2})', files)[0]
axs[2, 1].plot(state['year'], state['total cost ($)'], label = fr'{regex}')
axs[2, 1].fill_between(state['year'], state['total cost ($)'], alpha=0.1)
#axs[2].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[2, 1].set_title('Charlottesville, Virginia')
axs[2, 1].margins(x=0, y=5)
axs[2, 1].set(xlabel="Year", ylabel="Total cost ($)", ylim = (0, 26200000))
axs[2, 1].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor')
plt.gcf().subplots_adjust(bottom=0.06, right=0.91, left=0.06)
fig.savefig(os.path.join(cwd, f"selected_plants_26_years/Recycling_NAICS_capacity_analysis.png"), dpi=300, transparent=True);
fig.savefig(os.path.join(cwd, f"selected_plants_26_years/Recycling_NAICS_capacity_analysis.pdf"), dpi=300);
/var/folders/w2/85_h_nyn0mlbxf81x3k9n53xh90n_1/T/ipykernel_99819/3529915961.py:42: UserWarning: FixedFormatter should only be used together with FixedLocator axs[2, 0].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor') /var/folders/w2/85_h_nyn0mlbxf81x3k9n53xh90n_1/T/ipykernel_99819/3529915961.py:78: UserWarning: FixedFormatter should only be used together with FixedLocator axs[2, 1].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor')
fig, axs = plt.subplots(3, 2, figsize=(10,10),sharex=True)
fig.tight_layout(pad=1.5)
fig.suptitle("40209 Manufacturing plants - Cost Sensitivity Analysis",fontsize=20)
fig.subplots_adjust(top=0.92)
for files in manufacturing_files_cost:
if '40209' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Navajo, Arizona']
regex = re.findall('\w+_\w+_(\d{1,4})', files)[0]
axs[0, 0].plot(state['year'], state['utilization factor (%)'], label = fr'{regex}')
axs[0, 0].fill_between(state['year'], state['utilization factor (%)'], alpha=0.1)
#axs[0].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[0, 0].set_title('Navajo, Arizona')
axs[0, 0].margins(x=0)
# axs[0].xlabel('Year')
# axs[0].ylabel('Utilization factor (%)')
axs[0, 0].set(xlabel=" ", ylabel="Utilization factor (%)")
for files in manufacturing_files_cost:
if '40209' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'R D Morrow, Mississippi']
regex = re.findall('\w+_\w+_(\d{1,4})', files)[0]
axs[1, 0].plot(state['year'], state['utilization factor (%)'], label = fr'{regex}')
axs[1, 0].fill_between(state['year'], state['utilization factor (%)'], alpha=0.1)
#axs[2].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[1, 0].set_title('R D Morrow, Mississippi')
axs[1, 0].margins(x=0)
axs[1, 0].set(xlabel=" ", ylabel="Utilization factor (%)")
for files in manufacturing_files_cost:
if '40209' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Clinch River, Virginia']
regex = re.findall('\w+_\w+_(\d{1,4})', files)[0]
axs[2, 0].plot(state['year'], state['utilization factor (%)'], label = fr'{regex}')
axs[2, 0].fill_between(state['year'], state['utilization factor (%)'], alpha=0.1)
#axs[1, 0].legend(bbox_to_anchor=(1.1, 0.75), frameon=False)
axs[2, 0].set_title('Clinch River, Virginia')
axs[2, 0].margins(x=0)
axs[2, 0].set(xlabel=" ", ylabel="Utilization factor (%)")
axs[2, 0].set(xlabel="Year", ylabel="Utilization factor (%)", ylim = (0, 105))
axs[2, 0].set_xticklabels(labels=state['year'], rotation=45, ha='right', rotation_mode='anchor')
for files in manufacturing_files_cost:
if '40209' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Navajo, Arizona']
regex = re.findall('\w+_\w+_(\d{1,4})', files)[0]
axs[0, 1].plot(state['year'], state['total cost ($)'], label = fr'{regex}')
axs[0, 1].fill_between(state['year'], state['total cost ($)'], alpha=0.1)
#axs[0].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[0, 1].set_title('Navajo, Arizona')
axs[0, 1].margins(x=0)
# axs[0].xlabel('Year')
# axs[0].ylabel('Utilization factor (%)')
axs[0, 1].set(xlabel=" ", ylabel="Total cost ($)", ylim = (0, 2190000000))
axs[0, 1].yaxis.set_ticks(np.arange(0, 2190000000, 400000000))
for files in manufacturing_files_cost:
if '40209' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'R D Morrow, Mississippi']
regex = re.findall('\w+_\w+_(\d{1,4})', files)[0]
axs[1, 1].plot(state['year'], state['total cost ($)'], label = fr'{regex}')
axs[1, 1].fill_between(state['year'], state['total cost ($)'], alpha=0.1)
axs[1, 1].legend(bbox_to_anchor=(1.25, 0.75), frameon=False)
axs[1, 1].set_title('R D Morrow, Mississippi')
axs[1, 1].margins(x=0)
axs[1, 1].set(xlabel=" ", ylabel="Total cost ($)")
axs[1, 1].yaxis.set_ticks(np.arange(0, 2190000000, 400000000))
for files in manufacturing_files_cost:
if '40209' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Clinch River, Virginia']
regex = re.findall('\w+_\w+_(\d{1,4})', files)[0]
axs[2, 1].plot(state['year'], state['total cost ($)'], label = fr'{regex}')
axs[2, 1].fill_between(state['year'], state['total cost ($)'], alpha=0.1)
axs[2, 1].set_title('Clinch River, Virginia')
axs[2, 1].margins(x=0)
axs[2, 1].set(xlabel="Year", ylabel="Total cost ($)", ylim = (0, 2190000000))
axs[2, 1].yaxis.set_ticks(np.arange(0, 2190000000, 400000000))
axs[2, 1].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor')
plt.gcf().subplots_adjust(bottom=0.06, right=0.91, left=0.06)
fig.savefig(os.path.join(cwd, f"selected_plants_26_years/Manufacturing_40209_cost_analysis.png"), dpi=300, transparent=True);
fig.savefig(os.path.join(cwd, f"selected_plants_26_years/Manufacturing_40209_cost_analysis.pdf"), dpi=300);
/var/folders/w2/85_h_nyn0mlbxf81x3k9n53xh90n_1/T/ipykernel_99819/4082125229.py:41: UserWarning: FixedFormatter should only be used together with FixedLocator axs[2, 0].set_xticklabels(labels=state['year'], rotation=45, ha='right', rotation_mode='anchor') /var/folders/w2/85_h_nyn0mlbxf81x3k9n53xh90n_1/T/ipykernel_99819/4082125229.py:79: UserWarning: FixedFormatter should only be used together with FixedLocator axs[2, 1].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor')
plt.rcParams['axes.prop_cycle'] = plt.cycler(color=plt.cm.Set2.colors[2:5])
fig, axs = plt.subplots(3, 2, figsize=(10,10),sharex=True)
fig.tight_layout(pad=1.5)
fig.suptitle("40209 Manufacturing plants - Capacity Sensitivity Analysis",fontsize=20)
fig.subplots_adjust(top=0.92)
for files in manufacturing_files_cap:
if '40209' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Navajo, Arizona']
regex = re.findall('\w+_\w+_(\d{1,4})', files)[0]
axs[0, 0].plot(state['year'], state['utilization factor (%)'], label = fr'{regex}')
axs[0, 0].fill_between(state['year'], state['utilization factor (%)'], alpha=0.1)
#axs[0].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[0, 0].set_title('Navajo, Arizona')
axs[0, 0].margins(x=0)
# axs[0].xlabel('Year')
# axs[0].ylabel('Utilization factor (%)')
axs[0, 0].set(xlabel=" ", ylabel="Utilization factor (%)")
for files in manufacturing_files_cap:
if '40209' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'R D Morrow, Mississippi']
regex = re.findall('\w+_\w+_(\d{1,4})', files)[0]
axs[1, 0].plot(state['year'], state['utilization factor (%)'], label = fr'{regex}')
axs[1, 0].fill_between(state['year'], state['utilization factor (%)'], alpha=0.1)
#axs[2].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[1, 0].set_title('R D Morrow, Mississippi')
axs[1, 0].margins(x=0)
axs[1, 0].set(xlabel=" ", ylabel="Utilization factor (%)")
for files in manufacturing_files_cap:
if '40209' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Clinch River, Virginia']
regex = re.findall('\w+_\w+_(\d{1,4})', files)[0]
axs[2, 0].plot(state['year'], state['utilization factor (%)'], label = fr'{regex}')
axs[2, 0].fill_between(state['year'], state['utilization factor (%)'], alpha=0.1)
#axs[1, 0].legend(bbox_to_anchor=(1.1, 0.75), frameon=False)
axs[2, 0].set_title('Clinch River, Virginia')
axs[2, 0].margins(x=0)
axs[2, 0].set(xlabel=" ", ylabel="Utilization factor (%)")
axs[2, 0].set(xlabel="Year", ylabel="Utilization factor (%)", ylim = (0, 105))
axs[2, 0].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor')
for files in manufacturing_files_cap:
if '40209' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Navajo, Arizona']
regex = re.findall('\w+_\w+_(\d{1,4})', files)[0]
axs[0, 1].plot(state['year'], state['total cost ($)'], label = fr'{regex}')
axs[0, 1].fill_between(state['year'], state['total cost ($)'], alpha=0.1)
#axs[0].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[0, 1].set_title('Navajo, Arizona')
axs[0, 1].margins(x=0)
# axs[0].xlabel('Year')
# axs[0].ylabel('Utilization factor (%)')
axs[0, 1].set(xlabel=" ", ylabel="Total cost ($)", ylim = (0, 2190000000))
axs[0, 1].yaxis.set_ticks(np.arange(0, 2190000000, 400000000))
#axs[0, 1].xaxis.set_ticks(np.arange(2025, 2051))
for files in manufacturing_files_cap:
if '40209' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'R D Morrow, Mississippi']
regex = re.findall('\w+_\w+_(\d{1,4})', files)[0]
axs[1, 1].plot(state['year'], state['total cost ($)'], label = fr'{regex}')
axs[1, 1].fill_between(state['year'], state['total cost ($)'], alpha=0.1)
axs[1, 1].legend(bbox_to_anchor=(1.25, 0.75), frameon=False)
axs[1, 1].set_title('R D Morrow, Mississippi')
axs[1, 1].margins(x=0)
axs[1, 1].set(xlabel=" ", ylabel="Total cost ($)", ylim = (0, 2190000000))
axs[1, 1].yaxis.set_ticks(np.arange(0, 2190000000, 400000000))
for files in manufacturing_files_cap:
if '40209' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Clinch River, Virginia']
regex = re.findall('\w+_\w+_(\d{1,4})', files)[0]
axs[2, 1].plot(state['year'], state['total cost ($)'], label = fr'{regex}')
axs[2, 1].fill_between(state['year'], state['total cost ($)'], alpha=0.1)
axs[2, 1].set_title('Clinch River, Virginia')
axs[2, 1].margins(x=0)
axs[2, 1].set(xlabel="Year", ylabel="Total cost ($)", ylim = (0, 2190000000))
axs[2, 1].yaxis.set_ticks(np.arange(0, 2190000000, 400000000))
axs[2, 1].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor')
plt.gcf().subplots_adjust(bottom=0.06, right=0.91, left=0.06)
fig.savefig(os.path.join(cwd, f"selected_plants_26_years/Manufacturing_40209_capacity_analysis.png"), dpi=300, transparent=True);
fig.savefig(os.path.join(cwd, f"selected_plants_26_years/Manufacturing_40209_capacity_analysis.pdf"), dpi=300);
/var/folders/w2/85_h_nyn0mlbxf81x3k9n53xh90n_1/T/ipykernel_99819/2908762764.py:41: UserWarning: FixedFormatter should only be used together with FixedLocator axs[2, 0].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor') /var/folders/w2/85_h_nyn0mlbxf81x3k9n53xh90n_1/T/ipykernel_99819/2908762764.py:80: UserWarning: FixedFormatter should only be used together with FixedLocator axs[2, 1].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor')
plt.rcParams['axes.prop_cycle'] = plt.cycler(color=plt.cm.Set2.colors[:6])
fig, axs = plt.subplots(3, 2, figsize=(10,10),sharex=True)
fig.tight_layout(pad=1.5)
fig.suptitle("NAICS Manufacturing plants - Cost Sensitivity Analysis",fontsize=20)
fig.subplots_adjust(top=0.92)
for files in manufacturing_files_cost:
if 'NAICS' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Boye Knives, AZ']
regex = re.findall('\w+_\w+_(\d{1,4})', files)[0]
axs[0, 0].plot(state['year'], state['utilization factor (%)'], label = fr'{regex}')
axs[0, 0].fill_between(state['year'], state['utilization factor (%)'], alpha=0.1)
#axs[0].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[0, 0].set_title('Dolan Springs, Arizona')
axs[0, 0].margins(x=0)
# axs[0].xlabel('Year')
# axs[0].ylabel('Utilization factor (%)')
axs[0, 0].set(xlabel=" ", ylabel="Utilization factor (%)")
for files in manufacturing_files_cost:
if 'NAICS' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Bain Mfg. Co., Inc., MS']
regex = re.findall('\w+_\w+_(\d{1,4})', files)[0]
axs[1, 0].plot(state['year'], state['utilization factor (%)'], label = fr'{regex}')
axs[1, 0].fill_between(state['year'], state['utilization factor (%)'], alpha=0.1)
#axs[1, 0].legend(bbox_to_anchor=(1.1, 0.75), frameon=False)
axs[1, 0].set_title('Grenada, Missisipi')
axs[1, 0].margins(x=0)
axs[1, 0].set(xlabel=" ", ylabel="Utilization factor (%)")
# for ax in axs.flat:
# ax.set(xlabel='Year', ylabel='Utilization factor (%)')
for files in manufacturing_files_cost:
if 'NAICS' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Standard Enterprises, Inc., VA']
regex = re.findall('\w+_\w+_(\d{1,4})', files)[0]
axs[2, 0].plot(state['year'], state['utilization factor (%)'], label = fr'{regex}')
axs[2, 0].fill_between(state['year'], state['utilization factor (%)'], alpha=0.1)
#axs[2].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[2, 0].set_title('Charlottesville, Virginia')
axs[2, 0].margins(x=0)
axs[2, 0].set(xlabel="Year", ylabel="Utilization factor (%)")
axs[2, 0].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor')
for files in manufacturing_files_cost:
if 'NAICS' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Boye Knives, AZ']
regex = re.findall('\w+_\w+_(\d{1,4})', files)[0]
axs[0, 1].plot(state['year'], state['total cost ($)'], label = fr'{regex}')
axs[0, 1].fill_between(state['year'], state['total cost ($)'], alpha=0.1)
#axs[0].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[0, 1].set_title('Dolan Springs, Arizona')
axs[0, 1].margins(x=0)
# axs[0].xlabel('Year')
# axs[0].ylabel('Utilization factor (%)')
axs[0, 1].set(xlabel=" ", ylabel="Total cost ($)", ylim = (0, 2190000000))
axs[0, 1].yaxis.set_ticks(np.arange(0, 2190000000, 400000000))
for files in manufacturing_files_cost:
if 'NAICS' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Bain Mfg. Co., Inc., MS']
regex = re.findall('\w+_\w+_(\d{1,4})', files)[0]
axs[1, 1].plot(state['year'], state['total cost ($)'], label = fr'{regex}')
axs[1, 1].fill_between(state['year'], state['total cost ($)'], alpha=0.1)
axs[1, 1].legend(bbox_to_anchor=(1.25, 0.75), frameon=False)
axs[1, 1].set_title('Grenada, Missisipi')
axs[1, 1].margins(x=0)
axs[1, 1].set(xlabel=" ", ylabel="Total cost ($)", ylim = (0, 2190000000))
axs[2, 1].yaxis.set_ticks(np.arange(0, 2190000000, 400000000))
for files in manufacturing_files_cost:
if 'NAICS' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Standard Enterprises, Inc., VA']
regex = re.findall('\w+_\w+_(\d{1,4})', files)[0]
axs[2, 1].plot(state['year'], state['total cost ($)'], label = fr'{regex}')
axs[2, 1].fill_between(state['year'], state['total cost ($)'], alpha=0.1)
#axs[2].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[2, 1].set_title('Charlottesville, Virginia')
axs[2, 1].margins(x=0, y=5)
axs[2, 1].set(xlabel="Year", ylabel="Total cost ($)", ylim = (0, 2190000000))
axs[2, 1].yaxis.set_ticks(np.arange(0, 2190000000, 400000000))
axs[2, 1].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor')
plt.gcf().subplots_adjust(bottom=0.06, right=0.91, left=0.06)
fig.savefig(os.path.join(cwd, f"selected_plants_26_years/Manufacturing_NAICS_cost_analysis.png"), dpi=300, transparent=True);
fig.savefig(os.path.join(cwd, f"selected_plants_26_years/Manufacturing_NAICS_cost_analysis.pdf"), dpi=300);
/var/folders/w2/85_h_nyn0mlbxf81x3k9n53xh90n_1/T/ipykernel_99819/1183286205.py:42: UserWarning: FixedFormatter should only be used together with FixedLocator axs[2, 0].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor') /var/folders/w2/85_h_nyn0mlbxf81x3k9n53xh90n_1/T/ipykernel_99819/1183286205.py:81: UserWarning: FixedFormatter should only be used together with FixedLocator axs[2, 1].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor')
plt.rcParams['axes.prop_cycle'] = plt.cycler(color=plt.cm.Set2.colors[2:4])
fig, axs = plt.subplots(3, 2, figsize=(10,10),sharex=True)
fig.tight_layout(pad=1.5)
fig.suptitle("NAICS Manufacturing plants - Capacity Sensitivity Analysis",fontsize=20)
fig.subplots_adjust(top=0.92)
for files in manufacturing_files_cap:
if 'NAICS' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Boye Knives, AZ']
regex = re.findall('\w+_\w+_(\d{1,4})', files)[0]
axs[0, 0].plot(state['year'], state['utilization factor (%)'], label = fr'{regex}')
axs[0, 0].fill_between(state['year'], state['utilization factor (%)'], alpha=0.1)
#axs[0].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[0, 0].set_title('Dolan Springs, Arizona')
axs[0, 0].margins(x=0)
# axs[0].xlabel('Year')
# axs[0].ylabel('Utilization factor (%)')
axs[0, 0].set(xlabel=" ", ylabel="Utilization factor (%)")
for files in manufacturing_files_cap:
if 'NAICS' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Bain Mfg. Co., Inc., MS']
regex = re.findall('\w+_\w+_(\d{1,4})', files)[0]
axs[1, 0].plot(state['year'], state['utilization factor (%)'], label = fr'{regex}')
axs[1, 0].fill_between(state['year'], state['utilization factor (%)'], alpha=0.1)
#axs[1, 0].legend(bbox_to_anchor=(1.1, 0.75), frameon=False)
axs[1, 0].set_title('Grenada, Missisipi')
axs[1, 0].margins(x=0)
axs[1, 0].set(xlabel=" ", ylabel="Utilization factor (%)")
# for ax in axs.flat:
# ax.set(xlabel='Year', ylabel='Utilization factor (%)')
for files in manufacturing_files_cap:
if 'NAICS' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Standard Enterprises, Inc., VA']
regex = re.findall('\w+_\w+_(\d{1,4})', files)[0]
axs[2, 0].plot(state['year'], state['utilization factor (%)'], label = fr'{regex}')
axs[2, 0].fill_between(state['year'], state['utilization factor (%)'], alpha=0.1)
#axs[2].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[2, 0].set_title('Charlottesville, Virginia')
axs[2, 0].margins(x=0)
axs[2, 0].set(xlabel="Year", ylabel="Utilization factor (%)")
axs[2, 0].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor')
for files in manufacturing_files_cap:
if 'NAICS' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Boye Knives, AZ']
regex = re.findall('\w+_\w+_(\d{1,4})', files)[0]
axs[0, 1].plot(state['year'], state['total cost ($)'], label = fr'{regex}')
axs[0, 1].fill_between(state['year'], state['total cost ($)'], alpha=0.1)
#axs[0].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[0, 1].set_title('Dolan Springs, Arizona')
axs[0, 1].margins(x=0)
# axs[0].xlabel('Year')
# axs[0].ylabel('Utilization factor (%)')
axs[0, 1].set(xlabel=" ", ylabel="Total cost ($)", ylim = (0, 2190000000))
axs[0, 1].yaxis.set_ticks(np.arange(0, 2190000000, 400000000))
for files in manufacturing_files_cap:
if 'NAICS' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Bain Mfg. Co., Inc., MS']
regex = re.findall('\w+_\w+_(\d{1,4})', files)[0]
axs[1, 1].plot(state['year'], state['total cost ($)'], label = fr'{regex}')
axs[1, 1].fill_between(state['year'], state['total cost ($)'], alpha=0.1)
axs[1, 1].legend(bbox_to_anchor=(1.2, 0.75), frameon=False)
axs[1, 1].set_title('Grenada, Missisipi')
axs[1, 1].margins(x=0)
axs[1, 1].set(xlabel=" ", ylabel="Total cost ($)", ylim = (0, 2190000000))
axs[1, 1].yaxis.set_ticks(np.arange(0, 2190000000, 400000000))
for files in manufacturing_files_cap:
if 'NAICS' in files:
state = globals()['%s_plants' % (files)].loc[globals()['%s_plants' % (files)]['location name'] == 'Standard Enterprises, Inc., VA']
regex = re.findall('\w+_\w+_(\d{1,4})', files)[0]
axs[2, 1].plot(state['year'], state['total cost ($)'], label = fr'{regex}')
axs[2, 1].fill_between(state['year'], state['total cost ($)'], alpha=0.1)
#axs[2].legend(bbox_to_anchor=(1, 1.05), frameon=False)
axs[2, 1].set_title('Charlottesville, Virginia')
axs[2, 1].margins(x=0, y=5)
axs[2, 1].set(xlabel="Year", ylabel="Total cost ($)", ylim = (0, 2190000000))
axs[2, 1].yaxis.set_ticks(np.arange(0, 2190000000, 400000000))
axs[2, 1].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor')
plt.gcf().subplots_adjust(bottom=0.06, right=0.91, left=0.06)
fig.savefig(os.path.join(cwd, f"selected_plants_26_years/Manufacturing_NAICS_capacity_analysis.png"), dpi=300, transparent=True);
fig.savefig(os.path.join(cwd, f"selected_plants_26_years/Manufacturing_NAICS_capacity_analysis.pdf"), dpi=300);
/var/folders/w2/85_h_nyn0mlbxf81x3k9n53xh90n_1/T/ipykernel_99819/3216271169.py:42: UserWarning: FixedFormatter should only be used together with FixedLocator axs[2, 0].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor') /var/folders/w2/85_h_nyn0mlbxf81x3k9n53xh90n_1/T/ipykernel_99819/3216271169.py:81: UserWarning: FixedFormatter should only be used together with FixedLocator axs[2, 1].set_xticklabels(years_list, rotation=45, ha='right', rotation_mode='anchor')
plt.rcParams['axes.prop_cycle'] = plt.cycler(color=plt.cm.Set2.colors[:6])
Find the location files
p = Path(cwd)
p.parent.parent
PosixPath('/Users/mmendez/Documents/Postdoc/Software_dev/RICE')
locations_files_path = os.path.join(p.parent.parent, '2_data_preparation', 'RELOG_import_data', 'CandidateLocations')
data_40209 = pd.read_csv(os.path.join(locations_files_path, 'cl_40209_retired_plants.csv'))
data_NAICS = pd.read_csv(os.path.join(locations_files_path, 'cl_igate_single_loc_filter_naics.csv'))
world = gp.read_file(os.path.join(cwd, f'resources','USA_States_(Generalized)', 'USA_States_Generalized.shp'))
world = world.to_crs("EPSG:4326")
ax = world.plot(color="0.9", edgecolor="1", figsize=(14, 7))
ax.set_ylim([23, 50])
ax.set_xlim([-128, -65])
plt.axis('off')
plt.title('40209 Candidate Locations', fontdict = {'fontsize' : 20})
# Draw destination points
points = gp.points_from_xy(
data_40209["longitude (deg)"],
data_40209["latitude (deg)"],
)
gp.GeoDataFrame(data_40209, geometry=points).plot(ax=ax, color=my_purple, markersize=50, alpha=1)
plt.savefig(os.path.join(cwd, "maps_26_years",f"map_40209_candidates.png"), transparent=True, dpi=300);
plt.savefig(os.path.join(cwd, "maps_26_years",f"map_40209_candidates.pdf"), dpi=300);
world = gp.read_file(os.path.join(cwd, f'resources','USA_States_(Generalized)', 'USA_States_Generalized.shp'))
world = world.to_crs("EPSG:4326")
ax = world.plot(color="0.9", edgecolor="1", figsize=(14, 7))
ax.set_ylim([23, 50])
ax.set_xlim([-128, -65])
plt.axis('off')
plt.title('NAICS Candidate Locations', fontdict = {'fontsize' : 20})
# Draw destination points
points = gp.points_from_xy(
data_NAICS["longitude (deg)"],
data_NAICS["latitude (deg)"],
)
gp.GeoDataFrame(data_NAICS, geometry=points).plot(ax=ax, color=my_pink, markersize=50, alpha=1)
plt.savefig(os.path.join(cwd, "maps_26_years",f"map_NAICS_candidates.png"), transparent=True, dpi=300);
plt.savefig(os.path.join(cwd, "maps_26_years",f"map_NAICS_candidates.pdf"), dpi=300);
# Plot base map
world = gp.read_file(os.path.join(cwd, f'resources','USA_States_(Generalized)', 'USA_States_Generalized.shp'))
world = world.to_crs("EPSG:4326")
ax = world.plot(color="0.9", edgecolor="1", figsize=(14, 7))
ax.set_ylim([23, 50])
ax.set_xlim([-128, -65])
plt.axis('off')
ax.set(title=f'Recycling_40209_1_transportation')
# Draw transportation lines
data = Recycling_40209_1_transportation
lines = [
[
(
row["source longitude (deg)"],
row["source latitude (deg)"],
),
(
row["destination longitude (deg)"],
row["destination latitude (deg)"],
),
]
for (index, row) in data.iterrows()
]
ax.add_collection(
collections.LineCollection(
lines,
linewidths=0.005,
zorder=1,
alpha=0.7,
color=my_orange,
)
)
# Draw source points
points = gp.points_from_xy(
data["source longitude (deg)"],
data["source latitude (deg)"],
)
gp.GeoDataFrame(data, geometry=points).plot(ax=ax, marker='D',color=my_orange, markersize=10, edgecolor='white', linewidth=0.5)
# Draw destination points
points = gp.points_from_xy(
data["destination longitude (deg)"],
data["destination latitude (deg)"],
)
gp.GeoDataFrame(data, geometry=points).plot(ax=ax, markersize=60, color=my_orange, edgecolor='white', linewidth=0.5)
legend_elements = [Line2D([0], [0], color='lightsalmon', lw=1, label='Transport route'),
Line2D([0], [0], marker='D', color=my_orange, label='Collection center', markersize=3, linestyle='None'),
Line2D([0], [0], marker='o', color=my_orange, label='Recycling center', markersize=10, linestyle='None')]
ax.legend(handles=legend_elements, frameon=False, bbox_to_anchor=(0.35, 0.25))
<matplotlib.legend.Legend at 0x1505d7070>
# Plot base map
world = gp.read_file(os.path.join(cwd, f'resources','USA_States_(Generalized)', 'USA_States_Generalized.shp'))
world = world.to_crs("EPSG:4326")
ax = world.plot(color="0.9", edgecolor="1", figsize=(14, 7))
ax.set_ylim([23, 50])
ax.set_xlim([-128, -65])
plt.axis('off')
ax.set(title=f'Recycling_40209_1_transportation')
# Draw transportation lines
data = Recycling_40209_1_transportation
lines = [
[
(
row["source longitude (deg)"],
row["source latitude (deg)"],
),
(
row["destination longitude (deg)"],
row["destination latitude (deg)"],
),
]
for (index, row) in data.iterrows()
]
ax.add_collection(
collections.LineCollection(
lines,
linewidths=0.005,
zorder=1,
alpha=0.7,
color=my_tiel,
)
)
# Draw source points
points = gp.points_from_xy(
data["source longitude (deg)"],
data["source latitude (deg)"],
)
gp.GeoDataFrame(data, geometry=points).plot(ax=ax, marker='D',color=my_tiel, markersize=10, edgecolor='white', linewidth=0.5)
# Draw destination points
points = gp.points_from_xy(
data["destination longitude (deg)"],
data["destination latitude (deg)"],
)
gp.GeoDataFrame(data, geometry=points).plot(ax=ax, markersize=60, color=my_tiel, edgecolor='white', linewidth=0.5)
legend_elements = [Line2D([0], [0], color='lightseagreen', lw=1, label='Transport route'),
Line2D([0], [0], marker='D', color=my_tiel, label='Collection center', markersize=3, linestyle='None'),
Line2D([0], [0], marker='o', color=my_tiel, label='Recycling center', markersize=10, linestyle='None')]
ax.legend(handles=legend_elements, frameon=False, bbox_to_anchor=(0.35, 0.25))
<matplotlib.legend.Legend at 0x14c17a9b0>
year_open = []
location_open = []
latitude_open = []
longitude_open = []
scenario_open = []
for files in files_list:
for rows in globals()['%s_plants' % (files)][['year', 'location name','opening cost ($)', 'latitude (deg)', 'longitude (deg)']].iterrows():
if rows[1][2] != 0:
year_open.append(rows[1][0]) #year
location_open.append(rows[1][1]) #location
#rows[1][2] #amount
latitude_open.append(rows[1][3]) #lat
longitude_open.append(rows[1][4]) #long
scenario_open.append(files)
openings = pd.DataFrame({'Opening year':year_open,
'Location name':location_open,
'latitude (deg)':latitude_open,
'longitude (deg)':longitude_open,
'Scenario':scenario_open})
files_list
['Manufacturing_NAICS_0001', 'Manufacturing_NAICS_05', 'Manufacturing_NAICS_1', 'Manufacturing_NAICS_2', 'Manufacturing_40209_0001', 'Manufacturing_40209_05', 'Manufacturing_40209_1', 'Manufacturing_40209_2', 'Recycling_NAICS_05', 'Recycling_NAICS_1', 'Recycling_NAICS_2', 'Recycling_NAICS_5', 'Recycling_NAICS_10', 'Recycling_40209_05', 'Recycling_40209_1', 'Recycling_40209_2', 'Recycling_40209_5', 'Recycling_40209_10', 'Manufacturing_cap_NAICS_05', 'Manufacturing_cap_NAICS_1', 'Manufacturing_cap_NAICS_2', 'Manufacturing_cap_40209_05', 'Manufacturing_cap_40209_1', 'Manufacturing_cap_40209_2', 'Recycling_cap_NAICS_05', 'Recycling_cap_NAICS_1', 'Recycling_cap_NAICS_2', 'Recycling_cap_NAICS_5', 'Recycling_cap_NAICS_10', 'Recycling_cap_40209_05', 'Recycling_cap_40209_1', 'Recycling_cap_40209_2', 'Recycling_cap_40209_5', 'Recycling_cap_40209_10']
openings_recycling_cost_40209 = openings[openings['Scenario'].str.match('Recycling_40209')]
openings_recycling_cost_NAICS = openings[openings['Scenario'].str.match('Recycling_NAICS')]
openings_recycling_cap_40209 = openings[openings['Scenario'].str.match('Recycling_cap_40209')]
openings_recycling_cap_NAICS = openings[openings['Scenario'].str.match('Recycling_cap_NAICS')]
openings_manufacturing_cost_40209 = openings[openings['Scenario'].str.match('Manufacturing_40209')]
openings_manufacturing_cost_NAICS = openings[openings['Scenario'].str.match('Manufacturing_NAICS')]
openings_manufacturing_cap_40209 = openings[openings['Scenario'].str.match('Manufacturing_cap_40209')]
openings_manufacturing_cap_NAICS = openings[openings['Scenario'].str.match('Manufacturing_cap_NAICS')]
openings_recycling_cost_40209
| Opening year | Location name | latitude (deg) | longitude (deg) | Scenario | |
|---|---|---|---|---|---|
| 1071 | 2025 | Glen Lyn, Virginia | 37.318072 | -80.698321 | Recycling_40209_05 |
| 1072 | 2040 | H B Robinson, South Carolina | 34.332185 | -79.962115 | Recycling_40209_05 |
| 1073 | 2028 | San Juan, New Mexico | 36.511624 | -108.324578 | Recycling_40209_05 |
| 1074 | 2043 | St Johns River Power Park, Florida | 30.335245 | -81.648113 | Recycling_40209_05 |
| 1075 | 2027 | Lansing Smith, Florida | 30.238218 | -85.631680 | Recycling_40209_05 |
| ... | ... | ... | ... | ... | ... |
| 1477 | 2032 | Chesterfield, Virginia | 37.378434 | -77.585847 | Recycling_40209_10 |
| 1478 | 2043 | Jefferies, South Carolina | 33.207700 | -79.953655 | Recycling_40209_10 |
| 1479 | 2034 | Henderson I, Kentucky | 37.792542 | -87.572577 | Recycling_40209_10 |
| 1480 | 2034 | Vanderbilt University Power Plant, Tennessee | 36.169129 | -86.784790 | Recycling_40209_10 |
| 1481 | 2048 | Unifi Kinston LLC, North Carolina | 35.240068 | -77.635510 | Recycling_40209_10 |
411 rows × 5 columns
plt.subplots(figsize=(12,6), dpi=300)
fig = sns.countplot(data=openings_recycling_cost_40209, x="Opening year", hue="Scenario", width=1)
hatches = itertools.cycle(['////', '\\\\', '.O.O', '---', '...'])
for bars, hatch in zip(fig.containers, hatches):
# Set a different hatch for each group of bars
for bar in bars:
bar.set_hatch(hatch)
fig.set(title='40209 Recycling - Cost Analysis', ylabel="Number of plants opening per year", ylim = (0, 46))
fig.margins(x=0.005)
plt.xticks(rotation=45);
plt.tick_params(
axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
labelbottom=True) # labels along the bottom edge are off
plt.legend(['0.5', '1', '2', '5', '10'], loc='upper right', frameon=False)
plt.savefig(os.path.join(cwd, 'openings_26_years', 'openings_recycling_cost_40209.png'), transparent=True, dpi=300);
plt.savefig(os.path.join(cwd, 'openings_26_years', 'openings_recycling_cost_40209.pdf'), dpi=300);
years_list_cat = years_list.astype('str')
years_list_cat
array(['2025', '2030', '2035', '2040', '2045', '2050'], dtype='<U21')
plt.subplots(figsize=(12,6), dpi=300)
fig = sns.countplot(data=openings_recycling_cap_40209, x="Opening year", hue="Scenario", width=1)
hatches = itertools.cycle(['////', '\\\\', '.O.O', '---', '...'])
for bars, hatch in zip(fig.containers, hatches):
# Set a different hatch for each group of bars
for bar in bars:
bar.set_hatch(hatch)
fig.set(title='40209 Recycling - Capacity Analysis', ylabel="Number of plants opening per year", ylim = (0, 46))
fig.margins(x=0.005)
plt.xticks(years_list_cat, rotation=45);
plt.tick_params(
axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
labelbottom=True) # labels along the bottom edge are off
plt.legend(['0.5', '1', '2', '5', '10'], loc='upper right', frameon=False)
plt.savefig(os.path.join(cwd, 'openings_26_years', 'openings_recycling_cap_40209.png'), transparent=True, dpi=300);
plt.savefig(os.path.join(cwd, 'openings_26_years', 'openings_recycling_cap_40209.pdf'), dpi=300);
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) File ~/miniconda3/envs/RELOG/lib/python3.10/site-packages/matplotlib/axis.py:1506, in Axis.convert_units(self, x) 1505 try: -> 1506 ret = self.converter.convert(x, self.units, self) 1507 except Exception as e: File ~/miniconda3/envs/RELOG/lib/python3.10/site-packages/matplotlib/category.py:49, in StrCategoryConverter.convert(value, unit, axis) 48 if unit is None: ---> 49 raise ValueError( 50 'Missing category information for StrCategoryConverter; ' 51 'this might be caused by unintendedly mixing categorical and ' 52 'numeric data') 53 StrCategoryConverter._validate_unit(unit) ValueError: Missing category information for StrCategoryConverter; this might be caused by unintendedly mixing categorical and numeric data The above exception was the direct cause of the following exception: ConversionError Traceback (most recent call last) Cell In [118], line 12 10 fig.set(title='40209 Recycling - Capacity Analysis', ylabel="Number of plants opening per year", ylim = (0, 46)) 11 fig.margins(x=0.005) ---> 12 plt.xticks(years_list_cat, rotation=45); 13 plt.tick_params( 14 axis='x', # changes apply to the x-axis 15 which='both', # both major and minor ticks are affected 16 bottom=False, # ticks along the bottom edge are off 17 top=False, # ticks along the top edge are off 18 labelbottom=True) # labels along the bottom edge are off 19 plt.legend(['0.5', '1', '2', '5', '10'], loc='upper right', frameon=False) File ~/miniconda3/envs/RELOG/lib/python3.10/site-packages/matplotlib/pyplot.py:1809, in xticks(ticks, labels, **kwargs) 1806 raise TypeError("xticks(): Parameter 'labels' can't be set " 1807 "without setting 'ticks'") 1808 else: -> 1809 locs = ax.set_xticks(ticks) 1811 if labels is None: 1812 labels = ax.get_xticklabels() File ~/miniconda3/envs/RELOG/lib/python3.10/site-packages/matplotlib/axes/_base.py:75, in _axis_method_wrapper.__set_name__.<locals>.wrapper(self, *args, **kwargs) 74 def wrapper(self, *args, **kwargs): ---> 75 return get_method(self)(*args, **kwargs) File ~/miniconda3/envs/RELOG/lib/python3.10/site-packages/matplotlib/axis.py:1855, in Axis.set_ticks(self, ticks, labels, minor, **kwargs) 1829 def set_ticks(self, ticks, labels=None, *, minor=False, **kwargs): 1830 """ 1831 Set this Axis' tick locations and optionally labels. 1832 (...) 1853 ticks. 1854 """ -> 1855 result = self._set_tick_locations(ticks, minor=minor) 1856 if labels is not None: 1857 self.set_ticklabels(labels, minor=minor, **kwargs) File ~/miniconda3/envs/RELOG/lib/python3.10/site-packages/matplotlib/axis.py:1804, in Axis._set_tick_locations(self, ticks, minor) 1800 def _set_tick_locations(self, ticks, *, minor=False): 1801 # see docstring of set_ticks 1802 1803 # XXX if the user changes units, the information will be lost here -> 1804 ticks = self.convert_units(ticks) 1805 for name, axis in self.axes._get_axis_map().items(): 1806 if self is axis: File ~/miniconda3/envs/RELOG/lib/python3.10/site-packages/matplotlib/axis.py:1508, in Axis.convert_units(self, x) 1506 ret = self.converter.convert(x, self.units, self) 1507 except Exception as e: -> 1508 raise munits.ConversionError('Failed to convert value(s) to axis ' 1509 f'units: {x!r}') from e 1510 return ret ConversionError: Failed to convert value(s) to axis units: array(['2025', '2030', '2035', '2040', '2045', '2050'], dtype='<U21')
plt.subplots(figsize=(12,6), dpi=300)
fig = sns.countplot(data=openings_recycling_cost_NAICS, x="Opening year", hue="Scenario", width=1)
hatches = itertools.cycle(['////', '\\\\', '.O.O', '---', '...'])
for bars, hatch in zip(fig.containers, hatches):
# Set a different hatch for each group of bars
for bar in bars:
bar.set_hatch(hatch)
fig.set(title='NAICS Recycling - Cost Analysis', ylabel="Number of plants opening per year", ylim = (0, 46))
fig.margins(x=0.005)
plt.xticks(rotation=45);
plt.tick_params(
axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
labelbottom=True) # labels along the bottom edge are off
plt.legend(['0.5', '1', '2', '5', '10'], loc='upper right', frameon=False)
plt.savefig(os.path.join(cwd, 'openings_26_years', 'openings_recycling_cost_NAICS.png'), transparent=True, dpi=300);
plt.savefig(os.path.join(cwd, 'openings_26_years', 'openings_recycling_cost_NAICS.pdf'), dpi=300);
plt.subplots(figsize=(12,6), dpi=300)
fig = sns.countplot(data=openings_recycling_cap_NAICS, x="Opening year", hue="Scenario", width=1)
fig.set(title='NAICS Recycling - Capacity Analysis', ylabel="Number of plants opening per year", ylim = (0, 46))
hatches = itertools.cycle(['////', '\\\\', '.O.O', '---', '...'])
fig.margins(x=0.005)
for bars, hatch in zip(fig.containers, hatches):
# Set a different hatch for each group of bars
for bar in bars:
bar.set_hatch(hatch)
plt.xticks(rotation=45);
plt.tick_params(
axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
labelbottom=True) # labels along the bottom edge are off
plt.legend(['0.5', '1', '2', '5', '10'], loc='upper right', frameon=False)
plt.savefig(os.path.join(cwd, 'openings_26_years', 'openings_recycling_cap_NAICS.png'), transparent=True, dpi=300);
plt.savefig(os.path.join(cwd, 'openings_26_years', 'openings_recycling_cap_NAICS.pdf'), dpi=300);